class_docstr: Collects samples for all policies and agents from a multi-agent env.
  
  This API is controlled by RolloutWorker objects to store all data
  generated by Environments and Policies/Models during rollout and
  postprocessing. It's purposes are to a) make data collection and
  SampleBatch/input_dict generation from this data faster, b) to unify
  the way we collect samples from environments and model (outputs), thereby
  allowing for possible user customizations, c) to allow for more complex
  inputs fed into different policies (e.g. multi-agent case with inter-agent
  communication channel).
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docstr: Returns total number of (individual) agent-steps taken so far.
  
  Thereby, a step in an N-agent multi-agent environment counts as N.
  If less than N agents have stepped (because some agents were not
  required to send actions), the count will be increased by less than N.
  The returned count contains everything that has not been built yet
  (and returned as MultiAgentBatches by the
  `try_build_truncated_episode_multi_agent_batch` or
  `postprocess_episode(build=True)` methods). After such build, this
  counter is reset to 0.
  
  Returns:
      int: The number of (individual) agent-steps taken in total in the
          environment(s) so far.
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code: def total_agent_steps(self) -> int:
      """Returns total number of (individual) agent-steps taken so far.
  
      Thereby, a step in an N-agent multi-agent environment counts as N.
      If less than N agents have stepped (because some agents were not
      required to send actions), the count will be increased by less than N.
      The returned count contains everything that has not been built yet
      (and returned as MultiAgentBatches by the
      `try_build_truncated_episode_multi_agent_batch` or
      `postprocess_episode(build=True)` methods). After such build, this
      counter is reset to 0.
  
      Returns:
          int: The number of (individual) agent-steps taken in total in the
              environment(s) so far.
      """
      raise NotImplementedError
  
file_path: ray/rllib/evaluation/collectors/sample_collector.py
imports: import logging
  from abc import ABCMeta, abstractmethod
  from typing import TYPE_CHECKING, Dict, List, Optional, Union
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.policy.policy_map import PolicyMap
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch
  from ray.rllib.utils.annotations import PublicAPI
  from ray.rllib.utils.typing import AgentID, EnvID, EpisodeID, PolicyID, TensorType
label_desc: Use image node_id 6 for calling the SampleCollector obj's underlying member method code with example usage: obj.total_agent_steps() without return types